{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extend Trend Following Strategy ML research (added sample weights, Purged CV, feature importance)\n",
    "\n",
    "This notebook extends Trend-Following notebook from Chapter3 by adding sample weights, Purged Cross-Validation, MDI, MDA, SFI feature importance plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the Hudson and Thames MlFinLab package\n",
    "import mlfinlab as ml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import pyfolio as pf\n",
    "import timeit\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.utils import resample\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV, KFold, StratifiedKFold\n",
    "from mlfinlab.feature_importance import (feature_importance_mean_decrease_impurity, \n",
    "                                        feature_importance_mean_decrease_accuracy, \n",
    "                                        feature_importance_sfi, plot_feature_importance)\n",
    "from mlfinlab.ensemble import SequentiallyBootstrappedBaggingClassifier\n",
    "from mlfinlab.sample_weights import get_weights_by_return, get_weights_by_time_decay\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read in Data\n",
    "We are using the dollar bars based off of the high quality HFT data we purchased. There is a sample of bars available in this branch as well. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in data\n",
    "data = pd.read_csv('official_data/dollar_bars.csv')\n",
    "data.index = pd.to_datetime(data['date_time'])\n",
    "data = data.drop('date_time', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data['2011-09-01':]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Fit a Primary Model: Trend Following\n",
    "Based on the simple moving average cross-over strategy.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute moving averages\n",
    "fast_window = 20\n",
    "slow_window = 50\n",
    "\n",
    "data['fast_mavg'] = data['close'].rolling(window=fast_window, min_periods=fast_window, center=False).mean()\n",
    "data['slow_mavg'] = data['close'].rolling(window=slow_window, min_periods=slow_window, center=False).mean()\n",
    "data.head()\n",
    "\n",
    "# Compute sides\n",
    "data['side'] = np.nan\n",
    "\n",
    "long_signals = data['fast_mavg'] >= data['slow_mavg'] \n",
    "short_signals = data['fast_mavg'] < data['slow_mavg'] \n",
    "data.loc[long_signals, 'side'] = 1\n",
    "data.loc[short_signals, 'side'] = -1\n",
    "\n",
    "# Remove Look ahead biase by lagging the signal\n",
    "data['side'] = data['side'].shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save the raw data\n",
    "raw_data = data.copy()\n",
    "\n",
    "# Drop the NaN values from our data set\n",
    "data.dropna(axis=0, how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1.0    45136\n",
       "-1.0    38712\n",
       "Name: side, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['side'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filter Events: CUSUM Filter\n",
    "Predict what will happen when a CUSUM event is triggered. Use the signal from the MAvg Strategy to determine the side of the bet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compute daily volatility\n",
    "daily_vol = ml.util.get_daily_vol(close=data['close'], lookback=50)\n",
    "\n",
    "# Apply Symmetric CUSUM Filter and get timestamps for events\n",
    "# Note: Only the CUSUM filter needs a point estimate for volatility\n",
    "cusum_events = ml.filters.cusum_filter(data['close'], threshold=daily_vol['2011-09-01':'2018-01-01'].mean()*0.5)\n",
    "\n",
    "# Compute vertical barrier\n",
    "vertical_barriers = ml.labeling.add_vertical_barrier(t_events=cusum_events, close=data['close'], num_days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/anaconda3/envs/research/lib/python3.7/site-packages/mlfinlab/labeling/labeling.py:124: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
      "  target = target.loc[t_events]\n",
      "2019-11-25 14:08:57.965024 100.0% apply_pt_sl_on_t1 done after 0.11 minutes. Remaining 0.0 minutes..\n"
     ]
    }
   ],
   "source": [
    "pt_sl = [1, 2]\n",
    "min_ret = 0.005\n",
    "triple_barrier_events = ml.labeling.get_events(close=data['close'],\n",
    "                                               t_events=cusum_events,\n",
    "                                               pt_sl=pt_sl,\n",
    "                                               target=daily_vol,\n",
    "                                               min_ret=min_ret,\n",
    "                                               num_threads=3,\n",
    "                                               vertical_barrier_times=vertical_barriers,\n",
    "                                               side_prediction=data['side'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0    4384\n",
       " 1.0    4013\n",
       "Name: side, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = ml.labeling.get_bins(triple_barrier_events, data['close'])\n",
    "labels.side.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Results of Primary Model:\n",
    "What is the accuracy of predictions from the primary model (i.e., if the sec- ondary model does not filter the bets)? What are the precision, recall, and F1-scores?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.00      0.00      0.00      5293\n",
      "           1       0.37      1.00      0.54      3104\n",
      "\n",
      "    accuracy                           0.37      8397\n",
      "   macro avg       0.18      0.50      0.27      8397\n",
      "weighted avg       0.14      0.37      0.20      8397\n",
      "\n",
      "Confusion Matrix\n",
      "[[   0 5293]\n",
      " [   0 3104]]\n",
      "\n",
      "Accuracy\n",
      "0.36965582946290343\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alex/anaconda3/envs/research/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "primary_forecast = pd.DataFrame(labels['bin'])\n",
    "primary_forecast['pred'] = 1\n",
    "primary_forecast.columns = ['actual', 'pred']\n",
    "\n",
    "# Performance Metrics\n",
    "actual = primary_forecast['actual']\n",
    "pred = primary_forecast['pred']\n",
    "print(classification_report(y_true=actual, y_pred=pred))\n",
    "\n",
    "print(\"Confusion Matrix\")\n",
    "print(confusion_matrix(actual, pred))\n",
    "\n",
    "print('')\n",
    "print(\"Accuracy\")\n",
    "print(accuracy_score(actual, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**A few takeaways**\n",
    "* There is an imbalance in the classes - more are classified as \"no trade\"\n",
    "* Meta-labeling says that there are many false-positives  \n",
    "* the sklearn's confusion matrix is [[TN, FP][FN, TP]] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Fit a Meta Model\n",
    "Train a random forest to decide whether to trade or not (i.e 1 or 0 respectively) since the earlier model has decided the side (-1 or 1)\n",
    "\n",
    "Create the following features: \n",
    "* Volatility\n",
    "* Serial Correlation\n",
    "* The returns at the different lags from the serial correlation\n",
    "* The sides from the SMavg Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>cum_vol</th>\n",
       "      <th>cum_dollar</th>\n",
       "      <th>cum_ticks</th>\n",
       "      <th>fast_mavg</th>\n",
       "      <th>slow_mavg</th>\n",
       "      <th>side</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date_time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-09-02 12:30:35.193</th>\n",
       "      <td>1192.00</td>\n",
       "      <td>1198.50</td>\n",
       "      <td>1182.00</td>\n",
       "      <td>1183.25</td>\n",
       "      <td>58894</td>\n",
       "      <td>70005676.00</td>\n",
       "      <td>18835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-02 12:34:43.063</th>\n",
       "      <td>1183.50</td>\n",
       "      <td>1185.50</td>\n",
       "      <td>1179.75</td>\n",
       "      <td>1180.25</td>\n",
       "      <td>59197</td>\n",
       "      <td>70003214.75</td>\n",
       "      <td>17995</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-02 12:46:13.588</th>\n",
       "      <td>1180.25</td>\n",
       "      <td>1185.25</td>\n",
       "      <td>1179.75</td>\n",
       "      <td>1182.50</td>\n",
       "      <td>59181</td>\n",
       "      <td>70015589.25</td>\n",
       "      <td>20245</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-02 13:16:37.863</th>\n",
       "      <td>1182.50</td>\n",
       "      <td>1184.00</td>\n",
       "      <td>1180.25</td>\n",
       "      <td>1180.50</td>\n",
       "      <td>59208</td>\n",
       "      <td>70001715.00</td>\n",
       "      <td>21976</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-02 13:31:19.243</th>\n",
       "      <td>1180.50</td>\n",
       "      <td>1182.00</td>\n",
       "      <td>1178.00</td>\n",
       "      <td>1181.25</td>\n",
       "      <td>59320</td>\n",
       "      <td>70000150.25</td>\n",
       "      <td>20169</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            open     high      low    close  cum_vol  \\\n",
       "date_time                                                              \n",
       "2011-09-02 12:30:35.193  1192.00  1198.50  1182.00  1183.25    58894   \n",
       "2011-09-02 12:34:43.063  1183.50  1185.50  1179.75  1180.25    59197   \n",
       "2011-09-02 12:46:13.588  1180.25  1185.25  1179.75  1182.50    59181   \n",
       "2011-09-02 13:16:37.863  1182.50  1184.00  1180.25  1180.50    59208   \n",
       "2011-09-02 13:31:19.243  1180.50  1182.00  1178.00  1181.25    59320   \n",
       "\n",
       "                          cum_dollar  cum_ticks  fast_mavg  slow_mavg  side  \n",
       "date_time                                                                    \n",
       "2011-09-02 12:30:35.193  70005676.00      18835        NaN        NaN   NaN  \n",
       "2011-09-02 12:34:43.063  70003214.75      17995        NaN        NaN   NaN  \n",
       "2011-09-02 12:46:13.588  70015589.25      20245        NaN        NaN   NaN  \n",
       "2011-09-02 13:16:37.863  70001715.00      21976        NaN        NaN   NaN  \n",
       "2011-09-02 13:31:19.243  70000150.25      20169        NaN        NaN   NaN  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log Returns\n",
    "raw_data['log_ret'] = np.log(raw_data['close']).diff()\n",
    "\n",
    "# Momentum\n",
    "raw_data['mom1'] = raw_data['close'].pct_change(periods=1)\n",
    "raw_data['mom2'] = raw_data['close'].pct_change(periods=2)\n",
    "raw_data['mom3'] = raw_data['close'].pct_change(periods=3)\n",
    "raw_data['mom4'] = raw_data['close'].pct_change(periods=4)\n",
    "raw_data['mom5'] = raw_data['close'].pct_change(periods=5)\n",
    "\n",
    "# Volatility\n",
    "raw_data['volatility_50'] = raw_data['log_ret'].rolling(window=50, min_periods=50, center=False).std()\n",
    "raw_data['volatility_31'] = raw_data['log_ret'].rolling(window=31, min_periods=31, center=False).std()\n",
    "raw_data['volatility_15'] = raw_data['log_ret'].rolling(window=15, min_periods=15, center=False).std()\n",
    "\n",
    "# Serial Correlation (Takes about 4 minutes)\n",
    "window_autocorr = 50\n",
    "\n",
    "raw_data['autocorr_1'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=1), raw=False)\n",
    "raw_data['autocorr_2'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=2), raw=False)\n",
    "raw_data['autocorr_3'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=3), raw=False)\n",
    "raw_data['autocorr_4'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=4), raw=False)\n",
    "raw_data['autocorr_5'] = raw_data['log_ret'].rolling(window=window_autocorr, min_periods=window_autocorr, center=False).apply(lambda x: x.autocorr(lag=5), raw=False)\n",
    "\n",
    "# Get the various log -t returns\n",
    "raw_data['log_t1'] = raw_data['log_ret'].shift(1)\n",
    "raw_data['log_t2'] = raw_data['log_ret'].shift(2)\n",
    "raw_data['log_t3'] = raw_data['log_ret'].shift(3)\n",
    "raw_data['log_t4'] = raw_data['log_ret'].shift(4)\n",
    "raw_data['log_t5'] = raw_data['log_ret'].shift(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Re compute sides\n",
    "raw_data['side'] = np.nan\n",
    "\n",
    "long_signals = raw_data['fast_mavg'] >= raw_data['slow_mavg']\n",
    "short_signals = raw_data['fast_mavg'] < raw_data['slow_mavg']\n",
    "\n",
    "raw_data.loc[long_signals, 'side'] = 1\n",
    "raw_data.loc[short_signals, 'side'] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove look ahead bias\n",
    "raw_data = raw_data.shift(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now get the data at the specified events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get features at event dates\n",
    "X = raw_data.loc[labels.index, :]\n",
    "\n",
    "# Drop unwanted columns\n",
    "X.drop(['open', 'high', 'low', 'close', 'cum_vol', 'cum_dollar', 'cum_ticks','fast_mavg', 'slow_mavg',], axis=1, inplace=True)\n",
    "\n",
    "y = labels['bin']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5293\n",
       "1    3104\n",
       "Name: bin, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training, validation and test sets\n",
    "X_training_validation = X['2011-09-01':'2018-01-01']\n",
    "y_training_validation = y['2011-09-01':'2018-01-01']\n",
    "X_train, X_validate, y_train, y_validate = train_test_split(X_training_validation, y_training_validation, test_size=0.15, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2019-11-25 14:17:48.599647 100.0% num_concurrent_events done after 0.02 minutes. Remaining 0.0 minutes.\n",
      "2019-11-25 14:17:49.851203 100.0% _apply_weight_by_return done after 0.02 minutes. Remaining 0.0 minutes.\n",
      "2019-11-25 14:17:51.186906 100.0% num_concurrent_events done after 0.02 minutes. Remaining 0.0 minutes.\n",
      "2019-11-25 14:17:52.079602 100.0% _get_average_uniqueness done after 0.01 minutes. Remaining 0.0 minutes.\n"
     ]
    }
   ],
   "source": [
    "return_based_sample_weights = get_weights_by_return(triple_barrier_events.loc[X_train.index], data.loc[X_train.index, 'close'])\n",
    "time_based_sample_weights = get_weights_by_time_decay(triple_barrier_events.loc[X_train.index], data.loc[X_train.index, 'close'], decay=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.title('Returns/Concurrency based sample weights')\n",
    "return_based_sample_weights.reset_index()[0].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "plt.title('Time decaying sample weights')\n",
    "time_based_sample_weights.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'max_depth':[2, 3, 4, 5, 7],\n",
    "              'n_estimators':[10, 25, 50, 100, 256, 512]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv_gen_standard = KFold(4)\n",
    "cv_gen_purged = ml.cross_validation.PurgedKFold(n_splits=4, \n",
    "                                                samples_info_sets=triple_barrier_events.loc[X_train.index].t1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_grid_search(X_data, y_data, cv_gen, scoring, type='standard'):\n",
    "    \"\"\"\n",
    "    Grid search using Purged CV without using sample weights in fit(). Returns top model and top score\n",
    "    \"\"\"\n",
    "    max_cross_val_score = -np.inf\n",
    "    top_model = None\n",
    "    for m_depth in parameters['max_depth']:\n",
    "        for n_est in parameters['n_estimators']:\n",
    "            clf_base = DecisionTreeClassifier(criterion='entropy', random_state=42, \n",
    "                                              max_depth=m_depth, class_weight='balanced')\n",
    "            if type == 'standard':\n",
    "                clf = BaggingClassifier(n_estimators=n_est, \n",
    "                                        base_estimator=clf_base, \n",
    "                                        random_state=42, n_jobs=-1, \n",
    "                                        oob_score=False, max_features=1.)\n",
    "            elif type == 'random_forest':\n",
    "                clf = RandomForestClassifier(n_estimators=n_est, \n",
    "                                             max_depth=m_depth, \n",
    "                                             random_state=42, \n",
    "                                             n_jobs=-1, \n",
    "                                             oob_score=False, \n",
    "                                            criterion='entropy',\n",
    "                                            class_weight='balanced_subsample', \n",
    "                                            max_features=1.)\n",
    "            elif type == 'sequential_bootstrapping':\n",
    "                clf = SequentiallyBootstrappedBaggingClassifier(samples_info_sets=triple_barrier_events.loc[X_train.index].t1,\n",
    "                                                                price_bars = data.loc[X_train.index.min():X_train.index.max(), 'close'],\n",
    "                                                                n_estimators=n_est, base_estimator=clf_base, \n",
    "                                                                random_state=42, n_jobs=-1, oob_score=False,\n",
    "                                                                max_features=1.)\n",
    "            temp_score_base = ml.cross_validation.ml_cross_val_score(clf, X_train, y_train, cv_gen, scoring=scoring)\n",
    "            if temp_score_base.mean() > max_cross_val_score:\n",
    "                max_cross_val_score = temp_score_base.mean()\n",
    "                print(temp_score_base.mean())\n",
    "                top_model = clf\n",
    "    return top_model, max_cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_grid_search_sample_weights(X_data, y_data, cv_gen, scoring, type='standard'):\n",
    "    \"\"\"\n",
    "    Grid search using Purged CV using sample weights in fit(). Returns top model and top score\n",
    "    \"\"\"\n",
    "    max_cross_val_score = -np.inf\n",
    "    top_model = None\n",
    "    for m_depth in parameters['max_depth']:\n",
    "        for n_est in parameters['n_estimators']:\n",
    "            clf_base = DecisionTreeClassifier(criterion='entropy', random_state=42, \n",
    "                                              max_depth=m_depth, class_weight='balanced')\n",
    "            if type == 'standard':\n",
    "                clf = BaggingClassifier(n_estimators=n_est, \n",
    "                                        base_estimator=clf_base, \n",
    "                                        random_state=42, n_jobs=-1, \n",
    "                                        oob_score=False, max_features=1.)\n",
    "            elif type == 'random_forest':\n",
    "                clf = RandomForestClassifier(n_estimators=n_est, \n",
    "                                             max_depth=m_depth, \n",
    "                                             random_state=42, \n",
    "                                             n_jobs=-1, \n",
    "                                             oob_score=False, \n",
    "                                            criterion='entropy',\n",
    "                                            class_weight='balanced_subsample', \n",
    "                                            max_features=1.)\n",
    "            elif type == 'sequential_bootstrapping':\n",
    "                clf = SequentiallyBootstrappedBaggingClassifier(samples_info_sets=triple_barrier_events.loc[X_train.index].t1,\n",
    "                                                                price_bars = data.loc[X_train.index.min():X_train.index.max(), 'close'],\n",
    "                                                                n_estimators=n_est, base_estimator=clf_base, \n",
    "                                                                random_state=42, n_jobs=-1, oob_score=False,\n",
    "                                                                max_features=1.)\n",
    "            temp_score_base = ml.cross_validation.ml_cross_val_score(clf, X_train, y_train, cv_gen, scoring=scoring,\n",
    "                                                                    sample_weight=return_based_sample_weights.values)\n",
    "            if temp_score_base.mean() > max_cross_val_score:\n",
    "                max_cross_val_score = temp_score_base.mean()\n",
    "                print(temp_score_base.mean())\n",
    "                top_model = clf\n",
    "    return top_model, max_cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.398540497902952\n",
      "0.44005908790011156\n"
     ]
    }
   ],
   "source": [
    "top_model, cross_val_score = perform_grid_search(X_train, y_train, cv_gen_purged, 'f1', type = 'standard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4225752089347531\n",
      "0.44954334973412946\n",
      "0.4536392517504336\n",
      "0.46023718653874335\n"
     ]
    }
   ],
   "source": [
    "top_model, cross_val_score = perform_grid_search_sample_weights(X_train, y_train, cv_gen_purged, 'f1', type = 'standard')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BaggingClassifier(base_estimator=DecisionTreeClassifier(class_weight='balanced',\n",
       "                                                        criterion='entropy',\n",
       "                                                        max_depth=2,\n",
       "                                                        max_features=None,\n",
       "                                                        max_leaf_nodes=None,\n",
       "                                                        min_impurity_decrease=0.0,\n",
       "                                                        min_impurity_split=None,\n",
       "                                                        min_samples_leaf=1,\n",
       "                                                        min_samples_split=2,\n",
       "                                                        min_weight_fraction_leaf=0.0,\n",
       "                                                        presort=False,\n",
       "                                                        random_state=42,\n",
       "                                                        splitter='best'),\n",
       "                  bootstrap=True, bootstrap_features=False, max_features=1.0,\n",
       "                  max_samples=1.0, n_estimators=100, n_jobs=-1, oob_score=False,\n",
       "                  random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see adding sample weights increases cross-validation score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MDI, MDA, SFI feature importance\n",
    "mdi_feat_imp = feature_importance_mean_decrease_impurity(top_model, X_train.columns)\n",
    "mda_feat_imp = feature_importance_mean_decrease_accuracy(top_model, X_train, y_train, cv_gen_purged, scoring='f1', \n",
    "                                                         sample_weight=return_based_sample_weights.values)\n",
    "sfi_feat_imp = feature_importance_sfi(top_model, X_train, y_train, cv_gen_purged, scoring='f1',\n",
    "                                     sample_weight=return_based_sample_weights.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(mdi_feat_imp, 0, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(mda_feat_imp, 0, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(sfi_feat_imp, 0, 0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
